Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli".
IRCCS-SDN.
Circ J. 2020 Jan 24;84(2):144-152. doi: 10.1253/circj.CJ-19-0879. Epub 2019 Dec 21.
Network medicine can advance current medical practice by arising as response to the limitations of a reductionist approach focusing on cardiovascular (CV) diseases as a direct consequence of a single defect. This molecular-bioinformatic approach integrates heterogeneous "omics" data and artificial intelligence to identify a chain of perturbations involving key components of multiple molecular networks that are closely related in the human interactome. The clinical view of the network-based approach is greatly supported by the general law of molecular interconnection governing all biological complex systems. Recent advances in bioinformatics have culminated in numerous quantitative platforms able to identify CV disease modules underlying perturbations of the interactome. This might provide novel insights in CV disease mechanisms as well as putative biomarkers and drug targets. We describe the network-based principles and discuss their application to classifying and treating common CV diseases. We compare the strengths and weaknesses of molecular networks in comparison with the classical current reductionist approach, and remark on the necessity for a two-way approach connecting network medicine with large clinical trials to concretely translate novel insights from bench to bedside.
网络医学可以通过对心血管疾病（CV）的单一缺陷的还原论方法的局限性的回应而前进，这是直接的结果。这种分子生物信息学方法整合了异构的“组学”数据和人工智能，以识别涉及多个分子网络关键组件的一连串扰动，这些组件在人类相互作用组中密切相关。基于网络的方法的临床观点得到了普遍的分子互联规律的极大支持，该规律控制着所有生物复杂系统。生物信息学的最新进展最终形成了许多定量平台，能够识别相互作用组扰动下的 CV 疾病模块。这可能为 CV 疾病机制以及潜在的生物标志物和药物靶点提供新的见解。我们描述了基于网络的原则，并讨论了它们在分类和治疗常见 CV 疾病中的应用。我们比较了分子网络与经典的当前还原论方法的优缺点，并指出需要一种双向方法将网络医学与大型临床试验联系起来，将从实验室到床边的新见解具体转化。
